Optimizing grippers for compensating pose uncertainties by dynamic simulation

Abstract

Gripper design process is one of the interesting challenges in the context of grasping within industry. Typically, simple parallel-finger grippers, which are easy to install and maintain, are used in platforms for robotic grasping. The context switches in these platforms require frequent exchange of gripper fingers to accommodate grasping of new products, while subjected to numerous constraints, such as workcell uncertainties due to the vision systems used. The design of these fingers consumes the man-hours of experienced engineers, and involves a lot of trial-and-error testing. In our previous work, we have presented a method to automatically compute the optimal finger shapes for defined task contexts in simulation. In this paper, we show the performance of our method in an industrial grasping scenario. We first analyze the uncertainties of the used vision system, which are the major source of grasping error. Then, we perform the experiments, both in simulation and in a real setting. The experiments confirmed the validity of our approach. The computed finger design was employed in a real industrial assembly scenario.

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

“…Further comparison of of results obtained for gripper designs tested in simulation and real-world environments is to be found in submitted works [36,37]. We are aiming at arriving at a software solution for computation of gripper designs that can substitute the tedious design process with potentially many iterations of trial and error, which is usually applied nowadays when creating robotic assembly solutions.…”